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An optimized facial recognition model for identifying criminal activities using deep learning strategy

International Journal of information technology (Singapore) 2023年第7期15卷 3907-3921页

作者： Gokulakrishnan, S. Chakrabarti, Prasun Hung, Bui Thanh Shankar, S. Siva Artificial Intelligence Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Deputy Provost ITM SLS Baroda University Gujarat Vadodara India Department of Computer Science and Engineering KG Reddy College of Engineering and Technology (Autonomous) Telangana Hyderabad India

The most important research field in the current period is facial recognition and detection. Furthermore, facial expression recognition applications are essential in various research areas such as criminal investigation, security, data base management systems (DBMS), innovative card application, and video surveillance systems. A criminal investigation is an important area of study. Thus, in today’s world, crime activities are fast expanding in response to psychological trauma. As a result, applying deep learning (DL) algorithms for face expression identification and crime activity monitoring is a growing area of investigation. In terms of pre-processing, feature extraction, and recognizing diverse facial emotions, DL produces better results. As a result, the current study seeks to offer a revolutionary intelligent Strawberry-based convolution neural network (SbCNN). When applied to the Kaggle face expression database, the created SbCNN technique performed best. Face verification and criminal face detection can be accomplished using this approach and the optimization fitness function. Even though the established design improves the robustness of feature extraction and classification, it is a time-consuming process. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Data base management system Deep learning Facial recognition and detection Kaggle Optimization Psychological harm Video surveillance systems

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The Empirical Evaluation of Artificial Intelligence-based Techniques for Improving Cyber Security 2

The Empirical Evaluation of Artificial Intelligence-based Te...

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2nd International Conference on Electronics and Renewable systems, ICEARS 2023

作者： Nayak, Chhaya Rao, Chintala Lakshmana Alam, Tanweer Singh, Shalini Islam, Shaziya Maginmani, Umme Habiba Pimpri Chinchwad College of Engineering Pune India School of Law Visakhapatnam India Islamic University of Madinah Faculty of Computer and Information Systems Department of Computer Science Madinah Saudi Arabia Graphic Era Deemed to Be University Department of Ece Uttarakhand Dehradun248002 India Rungta College of Engineering and Technology Bhilai India Secab Institute of Engineering and Technology Department of Computer Science and Engineering Karnataka Vijayapur India

ISBN: (纸本)9798350346640

Artificial intelligence (AI) technologies have been recently developed, and their advantages can be observed in a wide range of domains, from image processing to face detection. AI-based approaches can assist opponents to enhance their attack strategies while also improving cyber protection technologies. The efficiency of AI techniques against cyber security issues are evaluated. Here, the primary data and a quantitative study design approach is used. The information is then obtained from software industry professionals. The sample size for this study is 549 people, and it included confirmatory factor analysis, discriminant validity testing, model fundamental analysis, and hypothesis evaluation. All variables' P-values were found to be statistical, with the exception of intelligent agents, which had no meaningful relationship to AI or cyber security. The main issues were availability, geographical locations, study population, and other related variables. © 2023 IEEE.

关键词： Factor analysis

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OMNI- CHANNEL RETAILING FOR ENHANCING CUSTOMER ENGAGEMENT AMIDST SUPPLY CHAIN DISRUPTION: AN EMERGING MARKET PERSPECTIVE

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and engineering 2023年第1期6卷 449-473页

作者： Joshi, Sudhanshu Sharma, Manu Chatterjee, Prasanjit Operations and Supply Chain Management Research Laboratory School of Management Doon University Dehradun India School of Computer Science Faculty of Engineering & Information Technology University of Technology Sydney Sydney Australia Department of Management Studies Graphic Era Deemed to be University Dehradun India Guidhall School of Business and Law London Metropolitan University London United Kingdom MCKV Institute of Engineering West Bangal India

The research aims to explore the strength of enablers and adoption barriers in omnichannel retailing (OCR) and discuss how organizations may focus on redesigning their business models in emerging markets to manage the disruptive environment. The major enablers may enhance the omnichannel' performance to deliver a unified experience across all channels during the pandemic. The paper has used hybrid Multi-Criteria Decision-Making (MCDM) Methods. Organizations widely use these methods to explore the interrelationship among barriers and enablers affecting their performance. In the current study, 18 experts from different domains have examined and evaluated the 10 barriers and 7 enablers. The study reveals that integration, Visibility, internet accessibility, and advanced distribution centers are the primary enablers and driving the customer analytics enabler to strengthen their customer engagement and providing a unified experience to the. During the pandemic time the usage of the online channels have increased and thus retail channels may consider these enablers to enhance the unified experience level of the customers. The study also shows that inconsistency in price is the main adoption barrier followed by inconsistency in product discounts that should be minimized to engage customers effectively. The retail organizations need to understand the roadblocks in adopting OCR and should take relevant actions to minimize them. The retail organization or marketers may redesign their existing strategies based on price consistency, integration, Visibility, information systems, and coordination to develop a unified experience across channels during the pandemic situation. © 2023 by the authors.

关键词： Visibility

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Research on intrusion detection algorithm based on dilated convolution and bidirectional gating cyclic unit

Research on intrusion detection algorithm based on dilated c...

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2022 International Conference on Algorithms, Microchips and Network Applications

作者： Liu, Xingyuan Miao, Xianghua Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Computer Technology Application Key Laboratory of Yunnan Province Kunming650500 China

ISBN: (纸本)9781510653290

Intrusion detection based on machine learning plays an important role in the security protection of the network environment. In order to improve the accuracy of network intrusion detection, intrusion detection algorithm based on dilated convolution and a two-way gating unit is proposed. Firstly, dilated convolution (DCNN) is used to increase the receptive field of information and extract spatial features. Secondly, the bidirectional gated cyclic unit (bigru) model is used to capture the temporal features of data, and the attention mechanism is introduced to increase the calculation of feature importance. Compared with the classical machine learning classifier, this model has a high detection rate. The multi-classification experiments on the famous UNSW-NB15 data set show that the model has leading performance, and its classification accuracy can reach 97.14%. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Intrusion detection

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Mathematical Modelling of ANP for Trust Based IoT Device Categorization in Secured Smart Logistics

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Journal of Network and systems Management 2025年第3期33卷 1-35页

作者： Abbas, Arbab Waseem Marwat, Safdar Nawaz Khan Al-Fuqaha, Ala Brahim, Ghassen Ben Ahmed, Salman Khan, Waseem Ullah Institute of Computer Sciences and Information Technology Faculty of Management and Computer Sciences The University of Agriculture Peshawar Pakistan Department of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan College of Science and Engineering Hamad Bin Khalifa University Doha Qatar College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Kingdom of Saudi Arabia

In today’s era of massive information exchange, trust plays a significant role in the Internet of Things (IoT) security and privacy. Trust specifies the confidence of one point or network entity on another in a network model. As IoT devices of smart logistics rely on Internet connectivity, potential cyber-attacks pose a genuine threat. IoT devices have constrained capabilities, and therefore it is very difficult to differentiate between trustworthy and malicious IoT devices. The main contribution of this research is, first, to develop a mathematical model of the Analytical Network Process (ANP) for security and privacy in smart logistics. Second, IoT devices are categorized using trust as an attribute in the security domain of the smart logistics framework. Third, the applicability of ANP as a trust model for malicious IoT device identification is evaluated. And forth, for comparison, the Analytical Hierarchal Process (AHP) is applied to the same trust model for malicious IoT device identification, and results are compared with ANP. The proposed mathematical model of ANP for secured smart logistics is designed using three conceptual facets i.e., goal, criteria, and alternatives. The goal of the trust model is to identify malicious IoT devices using multi-criteria based on three trust parameters, i.e., Entity-Oriented Trust Model (EOTM), Data-Oriented Trust Model (DOTM), and Combined Trust Model (CTM). The alternatives in the trust model are the various candidate IoT devices. The architecture of the smart logistics framework has three modules i.e., internal IoT network, border router, and external network. The IoT devices communicate through the border router with the external world. Therefore, IoT devices are scanned by border router and taken as a trusted node in the secured smart logistics framework. Additionally, for comparison and validation, results obtained from ANP and AHP are evaluated. Results reveal a percentage difference of 16.86% for malicious IoT devic

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Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center

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Journal of computer Science & technology 2023年第4期38卷 773-792页

作者：田文洪徐敏贤周光耀吴逵须成忠 Rajkumar Buyya School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Computer Science University of VictoriaVictoriaBCV8W 3P6Canada State Key Laboratory of Internet of Things for Smart City University of MacaoMacao 999078China School of Computing and Information Systems University of MelbourneMelbourne 3010Australia

Load balancing is vital for the efficient and long-term operation of cloud data *** virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consoli***,it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring ***,we provide a new approach,called Prepartition,for load *** partitions a VM request into a few sub-requests sequentially with start time,end time and capacity demands,and treats each sub-request as a regular VM *** this way,it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal,which supports the resource allocation in a fine-grained *** with real-world trace and synthetic data show that our proposed approach with offline version(PrepartitionOff)scheduling has 10%–20%better performance than the existing load balancing baselines under several metrics,including average utilization,imbalance degree,makespan and Capacity_*** also extend Prepartition to online load *** results show that our proposed approach also outperforms state-of-the-art online algorithms.

关键词： cloud computing physical machine(PM) virtual machine(VM) reservation load balancing Prepartition

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A Novel Krill Herd Based Random Forest Algorithm for Monitoring Patient Health

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computers, Materials & Continua 2023年第5期75卷 4553-4571页

作者： Md.Moddassir Alam Md Mottahir Alam Muhammad Moinuddin Mohammad Tauheed Ahmad Jabir Hakami Anis Ahmad Chaudhary Asif Irshad Khan Tauheed Khan Mohd Department of Health Information Management and Technology University of Hafr Al-BatinSaudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing AbdulazizJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems King Abdul Aziz UniversityJeddahSaudi Arabia College of Medicine King Khalid UniversityAbhaSaudi Arabia Department of Physics College of ScienceJazan UniversityP.O.Box.114Jazan45142Saudi Arabia Department of Biology College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11623Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Math and Computer Science Augustana CollegeIL61201United States

Artificial Intelligence(AI)is finding increasing application in healthcare *** learning systems are utilized for monitoring patient health through the use of IoT sensor,which keep track of the physiological state by way of various health ***,early detection of any disease or derangement can aid doctors in saving patients’***,there are some challenges associated with predicting health status using the common algorithms,such as time requirements,chances of errors,and improper *** propose an Artificial Krill Herd based on the Random Forest(AKHRF)technique for monitoring patients’health and eliciting an optimal prescription based on their health *** begin with,various patient datasets were collected and trained into the system using IoT *** a result,the framework developed includes four processes:preprocessing,feature extraction,classification,and result ***,preprocessing removes errors,noise,and missing values from the dataset,whereas feature extraction extracts the relevant ***,in the classification layer,we updated the fitness function of the krill herd to classify the patient’s health status and also generate a *** found that the results fromthe proposed framework are comparable to the results from other state-of-the-art techniques in terms of sensitivity,specificity,Area under the Curve(AUC),accuracy,precision,recall,and F-measure.

关键词： Healthcare system health monitoring clinical decision support internet of things artificial intelligence machine learning diagnosis

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Desertification Detection in Makkah Region based on Aerial Images Classification

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computer systems Science & engineering 2022年第2期40卷 607-618页

作者： Yahia Said Mohammad Barr Taoufik Saidani Mohamed Atri Electrical Engineering Department College of EngineeringNorthern Border UniversityArarSaudi Arabia Laboratory of Electronics and Microelectronics(LR99ES30) Faculty of Sciences of MonastirUniversity of MonastirTunisia Department of Computer Sciences Faculty of Computing&Information TechnologyNorthern Border UniversityRafhaSaudi Arabia College of Computer Sciences King Khalid UniversityAbhaSaudi Arabia

Desertification has become a global threat and caused a crisis,especially in Middle Eastern countries,such as Saudi *** is one of the most important cities in Saudi Arabia that needs to be protected from *** vegetation area in Makkah has been damaged because of desertification through wind,floods,overgrazing,and global climate *** damage caused by desertification can be recovered provided urgent action is taken to prevent further degradation of the vegetation *** this paper,we propose an automatic desertification detection system based on Deep Learning *** images are classified using Convolutional Neural Networks(CNN)to detect land state variation in *** have been widely used for computer vision applications,such as image classification,image segmentation,and quality *** proposed CNN model was trained and evaluated on the Arial Image Dataset(AID).Compared to state-of-the-art methods,the proposed model has better performance while being suitable for embedded *** has achieved high efficiency with 96.47% *** light of the current research,we assert the appropriateness of the proposed CNN model in detecting desertification from aerial images.

关键词： Desertification detection deep learning convolutional neural networks(CNN) aerial images classification Makkah region

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Unveiling the Landscape of Machine Learning and Deep Learning Methodologies in Network Security: A Comprehensive Literature Review 2

Unveiling the Landscape of Machine Learning and Deep Learnin...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Saeed, Nouf Majid Sultan Eid Ibrahim, Amer Ali, Liaqat Al-Dmour, Nidal A. Mohammed, Abdul Salam Ghazal, Taher M. American University In The Emirates Dubai United Arab Emirates College Of Engineering And Technology University Of Science And Technology Of Fujairah Fujairah United Arab Emirates College Of Engineering Mutah University Department Of Computer Engineering Jordan Skyline University College University City Sharjah Shariah United Arab Emirates Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

The dynamic nature of cyber threats offers a continual problem in the field of cybersecurity in the context of the expanding internet environment. This study provides an in-depth assessment of the literature on machine learning (ML) and deep learning (DL) methodologies for network analysis for intrusion detection. This review curates, assesses, and distils method-specific findings while considering temporal or thermal correlations. It provides a recognition of the importance of data in ML and DL approaches, and a comprehensive overview of frequently used network datasets in ML/DL applications, as well as the inherent challenges of adopting ML/DL in the cybersecurity field. The study concludes with well-informed recommendations for future areas of research in this critical domine. © 2024 IEEE.

关键词： Intrusion detection

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WSports-50: An Image Dataset for Women’s Sport Action Classification 4th

WSports-50: An Image Dataset for Women’s Sport Action Class...

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4th International Conference on Frontiers in Computing and systems, COMSYS 2023

作者： Ray, Palash Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Nasipuri, Mita Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India Department of Computer Science and Information Systems BITS Pilani Rajasthan Pilani India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India

ISBN: (纸本)9789819726134

Sport action recognition is an interesting area in computer vision. Categorization of sport actions, representing difficult and complex body postures, is regarded as a fine-grained visual classification problem. The Convolutional Neural Networks (CNNs) have attained enhanced performance over conventional feature descriptors in recognizing various sport activities. In general, though decent improvement has been gained using deep learning for sport action recognition, however, recognition of women’s sport activities is not widely explored. Even, no benchmark dataset depicting women’s sport action with sufficient variations is available yet for study. Hence, fine-grained image classification of diverse sport categories involving female/women athletics requires immediate research attention. To overcome this limitation, this paper proposes an image dataset comprising worldwide popular 50 sport categories of women players only. A simple deep learning model is proposed that extracts the high-level deep features using a backbone CNN. Then, these features are pooled from a collection of regular regions representing local discriminative information. The spatial pyramid pooling is applied for mining semantic information and enhancing feature aggregation for classification. The proposed method has achieved satisfactory performance on the Women Sports dataset using four standard backbone CNNs. Moreover, our method has achieved better accuracy on the Yoga-82 pose recognition dataset with a significant margin, e.g., 11.6% gain using ResNet-50 base CNN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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